In addition to their descriptive power, subgraph densities are often used as features in generative probabilistic models characterizing an observed network. Subgraph densities are sufficient statistics for the exponential random graph model, for example, a model that is widely-used to describe social interactions and behavior [1]}, [2]}. Subgraph densities are also sufficient statistics for a large class of kernel-based random graphs [3]} and benefit from several useful properties that build the foundation of the large-sample analysis of the PCAN algorithm. We discuss these properties and our large-sample results in Section .
